Artificial Intelligence Enabled Personalized Stem Cell Therapy Recommendation Framework Integrating Genomics, Clinical Biomarkers and Multi-Omics Data for Precision Regenerative Medicine

Abstract

Precision regenerative medicine requires treatment decisions that account for molecular heterogeneity, clinical biomarkers, therapeutic dose, and patient-specific risk. This paper develops a research manuscript from the supplied result set for an artificial-intelligence-enabled stem cell therapy recommendation framework integrating genomics, transcriptomics, proteomics, metabolomics, epigenomics, and clinical biomarkers. The analytical cohort contains 1,248 evaluable records, partitioned into development (70%), internal validation (15%), and held-out test (15%) cohorts, with 4,742 retained features across six modalities. On the held-out test cohort, the proposed framework achieved 90.1% accuracy, 89.8% F1-score, 0.948 AUROC, 0.936 AUPRC, 91.2% sensitivity, and 88.4% specificity, exceeding the reported performance of logistic regression, random forest, support vector machine, XGBoost, artificial neural network, and multimodal transformer baselines. Ablation analysis showed progressive benefit from multimodal integration, with AUROC increasing from 0.792 for clinical biomarkers alone to 0.948 for full integration. Representative treatment-utility scores produced different therapy selections for distinct biological profiles. AUTO-07p outputs identified lower and upper switching thresholds and a Hopf transition, while twoparameter regions described predicted therapeutic windows. SHAP-based explainability identified inflammation, regenerative pathway activity, genomic repair risk, tissue-repair proteins, metabolic stress, and epigenetic regulation as leading feature groups. Robustness analyses showed moderate degradation under missing modalities and biomarker perturbation. The results support multimodal, interpretable, and dynamically constrained decision support, while also indicating the need for prospective external validation before clinical use.